A foundation for conventional and temporal query optimization addressing duplicates and ordering

Abstract

AbstractÐMost real-world databases contain substantial amounts of time-referenced, or temporal, data. Recent advances in temporal query languages show that such database applications may benefit substantially from built-in temporal support in the DBMS. To achieve this, temporal query representation, optimization, and processing mechanisms must be provided. This paper presents a foundation for query optimization that integrates conventional and temporal query optimization and is suitable for both conventional DBMS architectures and ones where the temporal support is obtained via a layer on top of a conventional DBMS. This foundation captures duplicates and ordering for all queries, as well as coalescing for temporal queries, thus generalizing all existing approaches known to the authors. It includes a temporally extended relational algebra to which SQL and temporal SQL queries may be mapped, six types of algebraic equivalences, concrete query transformation rules that obey different equivalences, a procedure for determining which types of transformation rules are applicable for optimizing a query, and a query plan enumeration algorithm. The presented approach partitions the work required by the database implementor to develop a provably correct query optimizer into four stages: The database implementor has to 1) specify operations formally, 2) design and prove correct appropriate transformation rules that satisfy any of the six equivalence types, 3) augment the mechanism that determines when the different types of rules are applicable to ensure that the enumeration algorithm applies the rules correctly, and 4) ensure that the mapping generates a correct initial query plan.

Query Processing Using Negative and Temporal Tuples in Stream Query Engines

“…In StreamAPAS, we develop a stream database which adapts the basis of relational databases and temporal databases [12,13,14] because they are also oriented towards processing ordered data. In contrast to other systems, our system processes both temporal positive tuples and the negative ones in a unitemporal stream model.…”

“Same, Same but Different” A Survey on Duplicate Detection Methods for Situation Awareness

“…Temporal databases, which store additional timestamps for describing an entry's validity and input time [29], describe temporal evolution of objects by multiple entries having adjacent valid times. To the best of our knowledge, in such temporal databases, like described in [30] and [4], duplicate detection is simplified to enable eliminating identical entries originating, e. g., from join-operators. We evaluate Slivinskas et al [4] explicitly proposing a temporal relational algebra handling such duplicate entries being characterized by identical properties with overlapping validity.…”

“…To the best of our knowledge, in such temporal databases, like described in [30] and [4], duplicate detection is simplified to enable eliminating identical entries originating, e. g., from join-operators. We evaluate Slivinskas et al [4] explicitly proposing a temporal relational algebra handling such duplicate entries being characterized by identical properties with overlapping validity. However, besides temporality, fuzzy information, object evolution, as well as context information are not discussed.…”

“…Based on our experience in this area 4 , examples from the domain of road traffic management are used to further illustrate the specific characteristics of situation awareness posing special requirements on duplicate detection. In principle, human operators of road traffic management systems observe highways for critical situations like traffic jams, relying on automated systems providing traffic information such as traffic flow detectors, but also on additional data sources like, e. g., motorists manually reporting traffic information to a call center.…”

“…A series of duplicate detection methods has been already proposed for a wide range of application domains including, e. g., databases in general [3], temporal and geospatial databases in specific [4], [5], data warehouses [6], data stream management systems [7], sensor networks [8], XML data [9], and ontologies [10]-each of them pursuing different goals and often, by nature, being heavily domain-specific. Nevertheless, the unique characteristics of situation awareness, comprising fuzzy information about real-world objects anchored in time and space, object evolution, and context information, together with their implications on the methods' applicability were not the main focus up to now.…”